Genetic Algorithms

1
Genetic Algorithms

• CIS 479/579
• Bruce R. Maxim
• UM-Dearborn

2
Genetic Algorithms

• What are they?
• Evolutionary algorithms that make use of
  operations like mutation, recombination, and
  selection
• Uses?
• Difficult search problems
• Optimization problems
• Machine learning
• Adaptive rule-bases

3
Genetic Algorithm Steps

• Potential solution for problem domains are
  encoded using machine representation (e.g. bit
  strings) that supports variation and selection
  operations
• Mating and mutation operations produce new
  generation of solutions from parent encodings
• Fitness function judges the individuals that are
  best suited (e.g. most appropriate problem
  solution) for survival

4
Operators

• Selection
• Want to to give preference to better
  individuals to add to mating pool
• If entire population ends up being selected it
  may be desirable to conduct a tournament order
  individuals in population
• Would like to keep the best in the mating pool
  and drop the worst
• Mutation
• The occasional (low probably) alteration of a bit
  position in a string

5
Operators

• Crossover
• If we have selected two strings
• A 11111 and B 00000
• We might choose a uniformly random site (e.g.
  position 3) and trade bits
• This would create two new strings
• A 11100 and B 00011
• These new strings might then be added to the
  mating pool if they are fit

6
Operators

• Selection and mutation
• When used together give us a genetic algorithm
  equivalent of to parallel, noise tolerant, hill
  climbing algorithm
• Selection, crossover, and mutation
• Provide an insurance policy against losing
  population diversity and avoiding some of the
  pitfalls of ordinary hill climbing

7
Genetic Algorithm

• Set time t 0
• Initialize population P(t)
• While termination condition not met
• Evaluate fitness of each member of P(t)
• Select members from P(t) based on fitness
• Produce offspring form the selected pairs
• Replace members of P(t) with better offspring
• Set time t t 1

8
Why use genetic algorithms?

• They can solve hard problems
• Easy to interface genetic algorithms to existing
  simulations and models
• GAs are extensible
• GAs are easy to hybridize
• GAs work by sampling, so populations can be
  sized to detect differences with specified error
  rates
• Use little problem specific code

9
TSP

• To use a genetic algorithm to solve the traveling
  salesman problem we could begin by creating a
  population of candidate solutions
• We need to define mutation, crossover, and
  selection methods to aid in evolving a solution
  from this population

10
TSP

• For crossover we might take two paths (P1 and P2)
  break them at arbitrary points and define new
  solutions Left1Right2 and Left2Right1
• For mutation we might randomly switch two cites
  in an existing path

11
Evolve Algorithm for TSP

• Set up initial population
• For G generations
• Create M mutations and add them to the population
• Subject mutations to population constraints and
  determine their relative fitness
• Create C crossovers and add them to the
  population
• Subject crossovers to population constraints and
  determine their relative fitness

12
Related Technologies

• Genetic Programming
• Existing programs are combined to breed new
  programs
• Artificial Life
• Using cellular automata to simulate population
  growth